CEMML - Fort Collins, CO

Colorado State University•Fort Collins, CO

3d•$78,000 - $84,000

About The Position

Colorado State University’s Center for Environmental Management of Military Lands (CEMML) is hiring a full-time Ecologist/Forestry Researcher for its Early-Career Development Program, which provides emerging professionals with hands-on experience, mentorship, and professional development. This is a research-focused position using GIS, remotely sensed data, and Forest Inventory and Analysis (FIA) data to characterize, map, model, and analyze tree species composition and related forest attributes in a tropical environment. The work will support understanding of forest ecosystem disturbance and resilience. The researcher will combine FIA data with existing spatial information and additional predictor variables, develop and evaluate spatial and model-assisted estimation approaches, and investigate tree species composition in areas where fires occurred. The position also involves comparing uncertainties in FIA estimates made with and without mapped auxiliary information, and preparing maps and related charts. The position does not supervise others. A CSU pre-employment background check is required. For full consideration, apply by November 23, 2026, at 11:59 PM Mountain Time.

Requirements

PhD in Systems Ecology or a related field.
Coursework in ecology, ecosystem climatology, statistics, spatial statistics, machine learning, applied bioinformatics, data visualization, geographic information systems, and science communication.
One year of experience in systems ecology, including work with GIS, remote sensing, and FIA plot data in tropical islands of the United States.
Experience as first author publishing a refereed journal article in a top research journal focused on work typically requiring geospatial data and analysis, such as Remote Sensing or Ecological Indicators.
Experience with programming languages including R and Python.
Successful completion of CSU’s standard pre-employment background check.

Nice To Haves

Experience developing spatial or statistical models of forest composition, biomass, basal area, or related ecological attributes.
Experience applying model-assisted estimation or other methods that integrate forest inventory data with auxiliary spatial information.
Experience integrating remote sensing, climate, topographic, fire, or other disturbance data in geospatial analyses.
Advanced proficiency using R, Python, and GIS for spatial modeling, uncertainty analysis, visualization, and reproducible workflows.
Experience communicating geospatial research through peer-reviewed publications, technical reports, maps, or presentations.

Responsibilities

Combine existing spatial data and additional predictor variables, as needed, with FIA data to characterize and map spatial gradients in tree species composition and related forest attributes.
Evaluate whether mapped outputs or model-derived spatial predictors can reduce uncertainty in FIA estimates of tree species basal area, biomass, or related composition metrics using one or more model-assisted estimation approaches.
Link mapped species-composition outputs to an existing fire-occurrence dataset to characterize tree species composition where fires occurred, including locations without field surveys of tree species.
Compare uncertainty in FIA estimates of species basal area, biomass, or related composition metrics using FIA plots alone with estimates using model-assisted mapped auxiliary information.
Create maps of model-derived species-composition gradients or related composition outputs.
Produce model-derived composition values for locations where fires occurred, with charts to characterize the composition of forests where fires occur.
Determine and develop appropriate model parameters.
Determine appropriate spatial predictor datasets.